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Approaches to Text Mining Arguments from Legal Cases

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Semantic Processing of Legal Texts

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6036))

Abstract

This paper describes recent approaches using text-mining to automatically profile and extract arguments from legal cases. We outline some of the background context and motivations. We then turn to consider issues related to the construction and composition of corpora of legal cases. We show how a Context-Free Grammar can be used to extract arguments, and how ontologies and Natural Language Processing can identify complex information such as case factors and participant roles. Together the results bring us closer to automatic identification of legal arguments.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom

    Adam Wyner

  2. Computer Science Department, Katholieke Universiteit Leuven, Afdeling Informatica, Celestijnenlaan, 3001, Heverlee, Belgium

    Raquel Mochales-Palau & Marie-Francine Moens

  3. Linguamatics Ltd, St. John’s Innovation Centre, Cowley Rd, Cambridge, CB4 0WS, United Kingdom

    David Milward

Authors
  1. Adam Wyner
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  2. Raquel Mochales-Palau
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  3. Marie-Francine Moens
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  4. David Milward
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Editor information

Editors and Affiliations

  1. Institute of Legal Information, Theory and Techniques, ITTIG-CNR, Via dei Barucci 20, 50127, Florence, Italy

    Enrico Francesconi  & Daniela Tiscornia  & 

  2. Istituto di Linguistica Computazionale "Antonio Zampolli" (ILC) - CNR, Area della Ricerca di Pisa,, Via Moruzzi 1, 56124, Pisa, Italy

    Simonetta Montemagni

  3. Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, S1 4DP, Sheffield, UK

    Wim Peters

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Wyner, A., Mochales-Palau, R., Moens, MF., Milward, D. (2010). Approaches to Text Mining Arguments from Legal Cases. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds) Semantic Processing of Legal Texts. Lecture Notes in Computer Science(), vol 6036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12837-0_4

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  • DOI: https://doi.org/10.1007/978-3-642-12837-0_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12836-3

  • Online ISBN: 978-3-642-12837-0

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